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Main workflow codecov



Digital Teaching Assistant

Setting Up Development Environment

Ubuntu 20.04 LTS

  1. Install Python 3.10. pyenv can help with switching among different Python versions.

  2. Install poetry and dependencies:

pip install poetry
poetry install
  1. Run tests, launch the app:
poetry shell
make test
make flask
  1. If you wish to seed the database, run:
poetry shell
make seed # python -m webapp.app --seed

Windows 10

  1. Install Python 3.10. Make sure python is added to PATH. You can check this by navigating to System (Control Panel) -> Advanced system settings -> Environment Variables -> System Variables -> PATH -> Edit.

  2. Install Chocolatey.

  3. Install GNU make:

choco install make
  1. Install poetry and dependencies:
pip install poetry
poetry install
  1. Run tests, launch the app:
poetry shell
make test
make flask-win
  1. If you wish to seed the database, run:
poetry shell
make seed # python -m webapp.app --seed

Acknoledgements

We appreciate all people who contributed to the project. Thanks to @Plintus-bit for designing the logo!

Architecture

The Digital Teaching Assistant system is described in the following papers:

  1. Sovietov P.N. Automatic Generation of Programming Exercises // In Proceedings of the 1st International Conference on Technology Enhanced Learning in Higher Education (TELE), 2021, pp. 111-114.

  2. Andrianova E.G., Demidova L.A., Sovetov P.N. Pedagogical design of a digital teaching assistant in massive professional training for the digital economy // Russian Technological Journal. 2022, 10 (3), pp. 7-23.

  3. Sovietov P.N., Gorchakov A.V. Digital Teaching Assistant for the Python Programming Course // In Proceedings of the 2nd International Conference on Technology Enhanced Learning in Higher Education (TELE), 2022, pp. 272-276.

  4. Demidova L.A., Sovietov P.N., Gorchakov A.V. Clustering of Program Source Text Representations Based on Markov Chains // Vestnik of Ryazan State Radio Engineering University. 2022, 81, pp. 51-64.

  5. Demidova L.A., Gorchakov A.V. Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines // Algorithms. 2022, 15 (9), p. 329.

  6. Gorchakov A.V., Demidova L.A., Sovietov P.N. Automated program text analysis using representations based on Markov chains and Extreme Learning Machines // Vestnik of Ryazan State Radio Engineering University. 2022, 82, pp. 89-103.